Academic Articles & Research Papers

Logical Reasoning Based on Residual Attention Multi-scale Relation Network

Logical reasoning is the ability to perceive patterns and connections between visual elements. Endowing computers with human-like reasoning ability is a critical area of research；state-of-the-art deep neural networks have achieved superhuma...

Evaluation of noise reduction methods in digital images: Classical filtering vs adaptive learning-based models

Digital images acquired through imaging systems often lose their original fidelity due to various types of noise introduced during acquisition, storage, or transmission, consequently reducing clarity and adversely affecting subsequent image...

Digital Local Return Services and Purchase Intention in Cross-Border E-Commerce: A Risk–Trust Perspective

Cross-border e-commerce offers consumers broader product access, yet uncertainty surrounding returns continues to suppress online purchase decisions. This study conceptualizes digital local return services as a digital assurance mechanism i...

Overview of Artificial Neural Networks

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to ...

Shear banding and flow instabilities in wormlike micelles: Modelling and mechanisms – A review

Worm-like micelles (WLMs) are dynamic, self-assembling supramolecular structures that exhibit complex viscoelastic behaviour due to their ability to undergo reversible scission, fusion, branching, and sequence rearrangement. This review pro...

Neural Networks in Chemistry

Abstract The capabilities of the human brain have always fascinated scientists and led them to investigate its inner workings. Over the past 50 years a number of models have been developed which have attempted to replicate the brain's vario...

Brain tumor grading based on Neural Networks and Convolutional Neural Networks

This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, wit...

neuralnet: Training of Neural Networks

Artificial neural networks are applied in many situations.neuralnet is built to train multi-layer perceptrons in the context of regression analyses, i.e. to approximate functional relationships between covariates and response variables.Thus...

Perception gaps between patients and practitioners regarding safety, data security, and ethics of intracranial electrode implantation: a multi-center study.

Predictive model for 5-year mortality after breast cancer surgery in Taiwan residents

Abstract Background Few studies of breast cancer surgery outcomes have used longitudinal data for more than 2 years. This study aimed to validate the use of the artificial neural network (ANN) model to predict the 5-year mortality of breast...

Protein structural model selection by combining consensus and single scoring methods.

Quality assessment (QA) for predicted protein structural models is an important and challenging research problem in protein structure prediction. Consensus Global Distance Test (CGDT) methods assess each decoy (predicted structural model) b...

Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS

Neural networks are important standard machine learning procedures for classification and regression. We describe the R package RSNNS that provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. The main feat...

Artificial Neural Networks

Abstract Artificial neural networks are useful tools that have been applied successfully in a broad range of signal‐processing problems. From a signal‐processing perspective, their ability to adapt continuously to new data allows them to tr...

On Calibration of Modern Neural Networks

Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike thos...

Tailoring Mission Effectiveness and Efficiency of a Ground Vehicle Using Exergy-Based Model Predictive Control (MPC)

To ensure dominance over a multi-domain battlespace, energy and power utilization must be accurately characterized for the dissimilar operational conditions. Using MATLAB/Simulink in combination with multiple neural networks, we created a m...

A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks

Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across...

Monthly Evapotranspiration Modeling in the Absence of Climatic Data Using the Soft Computing Methods in West and Northwest of Iran

Evapotranspiration (ET0), a major component of the hydrologic cycle, is important in water resources development and irrigation planning. The ET0 for west and northwest of Iran was estimated using the FAO Penman-Montieth method (FAO-56) as ...

Artificial neural networks

Artificial neural networks now are used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, ...

An Introduction to Neural Networks

An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain funct...

Neural networks and their applications

Neural networks provide a range of powerful new techniques for solving problems in pattern recognition, data analysis, and control. They have several notable features including high processing speeds and the ability to learn the solution to...